Publications
Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleCole JH, caan M, Underwood J, et al., 2018,
No evidence for accelerated ageing-related brain pathology in treated HIV: longitudinal neuroimaging results from the Comorbidity in Relation to AIDS (COBRA) project
, Clinical Infectious Diseases, Vol: 66, Pages: 1899-1909, ISSN: 1058-4838BackgroundDespite successful antiretroviral therapy people living with HIV (PLWH) experience higher rates of age-related morbidity, including abnormal brain structure, brain function and cognitive impairment. This has raised concerns that PLWH may experience accelerated ageing-related brain pathology.MethodsWe performed a multi-centre longitudinal study of 134 virologically-suppressed PLWH (median age = 56.0 years) and 79 demographically-similar HIV-negative controls (median age = 57.2 years). To measure cognitive performance and brain pathology, we conducted detailed neuropsychological assessments and multi-modality neuroimaging (T1-weighted, T2-weighted, diffusion-MRI, resting-state functional-MRI, spectroscopy, arterial spin labelling) at baseline and after two-year follow-up. Group differences in rates of change were assessed using linear mixed effects models.Results123 PLWH and 78 HIV-negative controls completed longitudinal assessments (median interval = 1.97 years). There were no differences between PLWH and HIV-negative controls in age, sex, years of education, smoking, alcohol use, recreational drug use, blood pressure, body-mass index or cholesterol levels.At baseline, PLWH had poorer global cognitive performance (P<0.01), lower grey matter volume (P=0.04), higher white matter hyperintensity load (P=0.02), abnormal white-matter microstructure (P<0.005) and greater ‘brain-predicted age difference’ (P=0.01). Longitudinally, there were no significant differences in rates of change in any neuroimaging measure between PLWH and HIV-negative controls (P>0.1). Cognitive performance was stable across the study period in both groups.ConclusionsOur finding indicate that when receiving successful treatment, middle-aged PLWH are not at increased risk of accelerated ageing-related brain changes or cognitive decline over two years, when compared to closely-matched HIV-negative controls.
-
Conference paperLi W, Lao-Kaim NP, Roussakis A, et al., 2018,
Functional connectivity changes in relation to dopaminergic decline in Parkinson's over time: a resting-state fMRI and 11C-PE2I PET imaging study
, 4th Congress of the European-Academy-of-Neurology (EAN), Publisher: WILEY, Pages: 345-345, ISSN: 1351-5101 -
Conference paperPuyol-Anton E, Ruijsink B, Bai W, et al., 2018,
Fully automated myocardial strain estimation from cine MRI using convolutional neural networks
, International Symposium on Biomedical Imaging, Pages: 1139-1143, ISSN: 1945-7928© 2018 IEEE. Cardiovascular magnetic resonance myocardial feature tracking (CMR-FT) is a promising method for quantification of cardiac function from standard steady-state free precession (SSFP) images. However, currently available techniques require operator dependent and time-consuming manual intervention, limiting reproducibility and clinical use. In this paper, we propose a fully automated pipeline to compute left ventricular (LV) longitudinal and radial strain from 2- and 4-chamber cine acquisitions, and LV circumferential and radial strain from the short-axis imaging. The method employs a convolutional neural network to automatically segment the myocardium, followed by feature tracking and strain estimation. Experiments are performed using 40 healthy volunteers and 40 ischemic patients from the UK Biobank dataset. Results show that our method obtained strain values that were in excellent agreement with the commercially available clinical CMR-FT software CVI42(Circle Cardiovascular Imaging, Calgary, Canada).
-
Journal articleWhittington A, Sharp DJ, Gunn RN, 2018,
Spatiotemporal distribution of β-amyloid in Alzheimer's disease results from heterogeneous regional carrying capacities
, Journal of Nuclear Medicine, Vol: 59, Pages: 822-827, ISSN: 1535-5667β-amyloid (Aβ) accumulation in the brain is one of two pathological hallmarks of Alzheimer's Disease (AD) and its spatial distribution has been studied extensively ex vivo. We apply mathematical modelling to Aβ in vivo PET imaging data in order to investigate competing theories of Aβ spread in AD. Our results provide evidence that Aβ accumulation starts in all brain regions simultaneously and that its spatiotemporal distribution is a result of heterogeneous regional carrying capacities (regional maximum possible concentration of Aβ) for the aggregated protein rather than longer term spreading from seed regions.
-
Journal articleSandrone S, van Gijn J, 2018,
Macdonald Critchley (1900-1997)
, JOURNAL OF NEUROLOGY, Vol: 265, Pages: 1244-1245, ISSN: 0340-5354 -
Journal articleLorenz R, Ribeiro Violante I, Monti R, et al., 2018,
Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization
, Nature Communications, Vol: 9, ISSN: 2041-1723Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.
-
Journal articleOliveira V, Kumutha JR E N, Somanna J, et al., 2018,
Hypothermia for encephalopathy in low-income and middle-income countries: feasibility of whole-body cooling using a low-cost servo-controlled device
, BMJ Paediatrics Open, Vol: 2, ISSN: 2399-9772Although therapeutic hypothermia (TH) is the standard of care for hypoxic ischaemic encephalopathy in high-income countries, the safety and efficacy of this therapy in low-income and middle-income countries (LMICs) is unknown. We aimed to describe the feasibility of TH using a low-cost servo-controlled cooling device and the short-term outcomes of the cooled babies in LMIC. Design: We recruited babies with moderate or severe hypoxic ischaemic encephalopathy (aged <6 hours) admitted to public sector tertiary neonatal units in India over a 28-month period. We administered whole-body cooling (set core temperature 33.5°C) using a servo-controlled device for 72 hours, followed by passive rewarming. We collected the data on short-term neonatal outcomes prior to hospital discharge. Results: Eighty-two babies were included-61 (74%) had moderate and 21 (26%) had severe encephalopathy. Mean (SD) hypothermia cooling induction time was 1.7 hour (1.5) and the effective cooling time 95% (0.08). The mean (SD) hypothermia induction time was 1.7 hour (1.5 hour), core temperature during cooling was 33.4°C (0.2), rewarming rate was 0.34°C (0.16°C) per hour and the effective cooling time was 95% (8%). Twenty-five (51%) babies had gastric bleeds, 6 (12%) had pulmonary bleeds and 21 (27%) had meconium on delivery. Fifteen (18%) babies died before discharge from hospital. Heart rate more than 120 bpm during cooling (P=0.01) and gastric bleeds (P<0.001) were associated with neonatal mortality. Conclusions: The low-cost servo-controlled cooling device maintained the core temperature well within the target range. Adequately powered clinical trials are required to establish the safety and efficacy of TH in LMICs. Clinical trial registration number: NCT01760629.
-
Journal articleMason SL, Daws RE, Soreq E, et al., 2018,
Predicting clinical diagnosis in Huntington's disease: An imaging polymarker
, ANNALS OF NEUROLOGY, Vol: 83, Pages: 532-543, ISSN: 0364-5134ObjectiveHuntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD.MethodA multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.ResultsClassification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models.InterpretationWe propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials.
-
Journal articleSandrone S, Cambiaghi M, 2018,
Ugo Cerletti (1877-1963)
, JOURNAL OF NEUROLOGY, Vol: 265, Pages: 731-732, ISSN: 0340-5354- Author Web Link
- Cite
- Citations: 1
-
Journal articleOktay O, Ferrante E, Kamnitsas K, et al., 2018,
Anatomically Constrained Neural Networks (ACNN): application to cardiac image enhancement and segmentation
, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 384-395, ISSN: 0278-0062Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.